招生方向：数学、统计（熟练C++、Python编程优先）
硕士研究生----深度贝叶斯方法(深度学习+贝叶斯); 科学机器学习方法；不确定性量化方法及应用；
统计反问题；数据同化；多保真建模及在可靠性分析、验证与确认（V&V）中的应用.
博士研究生----深度贝叶斯方法：理论、算法及其实现.
博士后----- 不确定性量化；贝叶斯建模及计算；科学机器学习
研究方向：不确定性量化、PDE反问题、贝叶斯建模及计算、科学机器学习
Research Interests（ResearchID，Google scholar）
－Uncertainty quantification
－Inverse and ill-posed problems
－Bayesian modeling and computation
－Scientific machine learning
Submitted:
4. Y. W.Yin, L. Yan, TDDM: A transfer learning framework for physics-guided 3D obstacle scattering inversion,2025.
3. S. L. Wu, L. Yan, T. Zhou, Z. Zhou, Operator learning based coarse solver for Parareal, 2025.
2. Y. W.Yin, L. Yan, A novel direct imaging method for passive inverse obstacle scattering problem,2024.
1. Y.Y. Wang, L.Yan, Data-driven operator inference for parameter estimation in nonlinear partial differential equation,2024.
Journal Papers:
42. Y.Y. Wang, L.Yan, T. Zhou, Deep learning-enhanced reduced-order ensemble Kalman filter for efficient Bayesian data assimilation of parametric PDEs, Computer Physics Communications, 311: 109544, 2025.
41. Y.W. Yin, L. Yan, Physics-aware deep learning framework for the limited aperture inverse obstacle scattering problem, SIAM J. Sci. Comput., 47(2):C313-C342,2025.
40. Z.W. Gao, L. Yan, T. Zhou, Adaptive operator learning for infinite-dimensional Bayesian inverse problems, SIAM/ASA J. Uncertainty Quantification, 12(4):1389-1423, 2024.
39. Y.W. Yin (尹运文), L. Yan, Bayesian model error method for the passive inverse scattering problem, Inverse Problem, 40: 065005, 2024.
38. H. Gu, X. Xu, L. Yan, Inverse elastic scattering by random periodic structures, J. Comput. Phy., 501: 112785, 2024.
37. W.B. Liu, L. Yan, T. Zhou, Y.C. Zhou, Failure-informed adaptive sampling for PINNs, Part III: applications to inverse problems, CSIAM Trans. Appl. Math., 5(3):636-670, 2024.
36. Z.W. Gao, T. Tang, L. Yan, T. Zhou, Failure-informed adaptive sampling for PINNs, Part II: combining with re-sampling and subset simulation, Commun. Appl. Math. Comput., 6: 1720-1741, 2024 (Invited contribution to a special issue for Prof. Remi Abgrall 's 61th birthday).
35. Z.W. Gao(高志伟), L. Yan, T. Zhou, Failure-informed adaptive sampling for PINNs, SIAM J. Sci. Comput., 45(4): A1971-A1994, 2023.(Highly Cited Paper，Hot Paper, code)
34. Y. Y. Wang(王艳艳), Q. Li(李倩), L.Yan, Adaptive ensemble Kalman inversion with statistical linearization, Commun. Comput. Phy., 33:1357-1380, 2023.
33. Y.C. Li(李勇超),Y. Y. Wang(王艳艳), L.Yan, Surrogate modeling for Bayesian inverse problems based on physics-informed neural networks, J. Comput. Phy., 475:111841, 2023.
32. L. Yan, T. Zhou, Stein variational gradient descent with local approximations, Comput. Meth. Appl. Mech. Eng., 386: 114087, 2021.
31. L. Yan, X.L. Zou(邹熙灵), Gradient-free Stein variational gradient descent with kernel approximation, Appl. Math. Letters, 121: 107465, 2021.
30. L. Yan, T. Zhou, An acceleration strategy for randomize-then-optimize sampling via deep neural networks, J. Comput. Math., 39(6):848-864, 2021.
29. A. Narayan, L. Yan, T. Zhou. Optimal design for the kernel interpolation: applications to uncertainty quantification, J. Comput. Phy., 430:110094, 2021.
28. L. Yan, T. Zhou, An adaptive surrogate modeling based on deep neural networks for large-scale Bayesian inverse problems, Commun. Comput. Phy., 28:2180-2205, 2020. (A special issue on Machine Learning for Scientific Computing)
27. F.L. Yang, L. Yan, A non-intrusive reduced basis EKI for time-fractional diffusion inverse problems, Acta Math. Appl.Sinica-English Serier, 36(1):183-202, 2020.(A special issue for IP)
26. L. Yan, T. Zhou. Adaptive multi-fidelity polynomial chaos approach to Bayesian inference in inverse problems,
J. Comput. Phy., 381: 110-128, 2019.
25. L.Yan, T. Zhou. An adaptive multi-fidelity PC-based ensemble Kalman inversion for inverse problems, Int. J. Uncertainty Quantification, 9(3):205-220, 2019.
24. Y.X. Zhang, J.X. Jian, L. Yan, Bayesian approach to a nonlinear inverse problem for time-space fractional diffusion equation, Inverse Problems, 34:125002(19pp), 2018.
23. F.L. Yang, L. Yan, L. Ling. Doubly stochastic radial basis function methods, J. Comput. Phy., 363: 87-97, 2018.
22. L. Guo, A. Narayan, L. Yan, T. Zhou.Weighted approximate Fekete points: sampling for least-squares polynomial approximation, SIAM J. Sci. Comput., 40 (1), A366-A387, 2018.
21. L. Yan, Y. X. Zhang. Convergence analysis of surrogate-based methods for Bayesian inverse problems, Inverse Problems, 33:125001(20pp), 2017.
20. L. Guo, Y. Liu, L. Yan, Sparse recovery via lq-minimization for polynomial chaos expansions, Numer. Math. Theor. Meth. Appl., 10(4):775-797, 2017.
19. L. Yan, Y. Shin, D. Xiu. Sparse approximation using L1-L2 minimization and its application to stochastic collocation SIAM J. Sci. Comput., 39(1): A229–A254, 2017.
18. Y.X.Zhang, L. Yan. The general a posteriori truncation method and its application to radiogenic source identification for the Helium production-diffusion equation, Appl. Math. Model., 43 :126-138, 2017.
17. J.J. Liu, M. Yamamoto, L. Yan. On the reconstruction of unknown boundary sources for time fractional diffusion process by nonlocal measurement. Inverse Problems, 32(1): 015009, 2016.
16. L. Yan, L. Guo. Stochastic collocation algorithms using l1-minimization for Bayesian solution of inverse problems, SIAM J. Sci. Comput., 37(3): A1410–A1435, 2015.
15. L. Yan, F. L. Yang. The method of approximate particular solutions for the time-fractional diffusion equation with a non-local boundary condition, Comput. Math. Appl., 70:254-264, 2015.
14. J.J.Liu, M. Yamamoto, L. Yan. On the uniqueness and reconstruction for an inverse problem of the fractional diffusion process, Appl. Numer. Math., 87:1-19, 2015.
13. L. Yan, F.L Yang. Efficient Kansa-type MFS algorithm for time-fractional inverse diffusion problems, Comput. Math. Appl.,2014, 67:1507-1520.
12. L. Yan, F.L. Yang. A Kansa-type MFS scheme for two-dimensional time fractional diffusion equations, Eng. Anal. Bound. Eleme., 2013, 37 (11): 1426–1435.
11. H. F. Zhao, L. Yan, J. J. Liu. On the interface identification of free boundary problem by method of fundamental solution. Numer. Linear Algebra Appl., 2013, 20(2)：385-396.
10. L. Yan, L. Guo, D.Xiu. Stochastic collocation algorithms using L1-minimization,Int. J. Uncertainty Quantification, 2012, 2(3): 279–293.(Highly Cited Paper)
9. L. Yan, F. L. Yang, C. L. Fu. A new numerical method for the inverse source problems from a Bayesian statistical perspective. Int. J. Numer. Meth. Eng., 2011, 85:1460-1474
8. Y.X. Zhang, C. L. Fu, L. Yan. Approximate inverse method for stable analytic continuation in a strip domain. J. Comput. Appl. Math., 2011, 235: 1979-1992
7. L. Yan, C. L. Fu, F. F. Dou. A computational method for identifying a spacewise-dependent heat source. Int. J. Numer. Meth. Biomedical Eng., 2010,26: 597-608
6. L. Yan, F. L.Yang, C.L.Fu. A meshless method for solving an inverse spacewise-dependent heat source problem. J. Comput. Phy., 2009, 228(1):123-136
5. F. L. Yang, L. Yan, T. Wei. The identification of a Robin coefficient by a conjugate gradient method. Int. J. Numer. Meth. Eng., 2009,78:800-816
4. L. Yan, F. L. Yang, C. L. Fu. A Bayesian inference approach to identify a Robin coefficient in one-dimensional parabolic problems. J. Comput. Appl. Math., 2009, 231(2):840-850
3. F. L. Yang, L. Yan, T. Wei. Reconstruction of part of a boundary for the Laplace equation by using a regularized method of fundamental solution. Inverse Problems Sci. Eng.,2009,17(8):1113-1128.
2. F. L. Yang, L. Yan, T. Wei. Reconstruction of the corrosion boundary for the Laplace equation by using a boundary collocation method. Math. Comput. Simu., 2009,79(7):2148-2156
1. L. Yan, C. L. Fu, F. L. Yang. The method of fundamental solutions for the inverse heat source problem. Eng. Anal. Bound. Elem., 2008, 32(3) :216-222.
